Method and system for adaptive resource allocation

ABSTRACT

Methods and systems for adaptively allocating resources within a communication network, such as adaptive zone allocation in a wireless backhaul network.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 61/408,602, filed Oct. 30, 2010 and entitled “Method andSystem for Adaptive Resource Allocation.”

FIELD OF THE INVENTION

The present invention relates to implementation of a communicationnetwork, and more specifically to adaptive zone allocation within acommunication network based on traffic demand.

BACKGROUND OF THE INVENTION

Communication between nodes in a communication network requires theallocation of resources for uplink and downlink traffic. As an example,traffic is transmitted and received between two nodes of a network. Inone case, a center node, or hub, controls communication with one or moreremote nodes, or remote stations, in the network.

For instance, the hub can use a time division duplex (TDD) framestructure for transferring information to and from the remote nodes. Theframe is divided into downlink (DL) and uplink (UL) sub-frames. Portionsof these sub-frames are further divided into zones or time sectors andare used to carry DL/UL traffic between two nodes in a communicationnetwork.

Allocation of these DL/UL zones is based on the number of connectedremote nodes. More specifically, allocation of the zones is staticallyapplied depending on the number of remote nodes. For instance, at eachnetwork entry or leave, available DL/UL resources are partitioned amongthe remote nodes equally to the extent possible. The zone allocationremains static as long as the number of connected remote nodes does notchange, and only changes when a remote node joins or leaves the networkconnection to the center node.

However, the allocation of zone resources does not take into accounttraffic activity, such as the change in the volume of trafficencountered by the remote nodes. Node traffic will vary depending onvarious variables, including time of day, activity of a population, andtype of community the network services. For instance, if a communicationnetwork including a controlling hub and one or more remote nodesservices a business district there will be high demand throughout themiddle of the day as persons work within the area, but less demand inthe early morning and late evenings as persons leave the area. Inaddition, certain areas served by remote nodes may encounter an increasein demand for traffic during certain periods. As an example, persons maytravel to various areas within the community to have lunch, therebycreating temporary increased in demand for traffic during those lunchhours. A static allocation of resources is unable to adequately handlean increase or decrease in demand from one remote node.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments are illustrated in referenced figures of thedrawings which illustrate what is regarded as the preferred embodimentspresently contemplated. It is intended that the embodiments and figuresdisclosed herein are to be considered illustrative rather than limiting.

FIG. 1 is an example of adaptive zone allocation numerology using 16symbols, in accordance with one embodiment of the present invention.

FIG. 2 is a flow diagram illustrating a method for adaptive resourceallocation for a new frame event, in accordance with one embodiment ofthe present invention.

FIG. 3 is a flow diagram illustrating a method for adaptive resourceallocation including actions that would take place for new frame events,in accordance with one embodiment of the present invention.

FIG. 4 is a flow diagram illustrating a method for adaptive resourceallocation including actions that would take place for remote node entryand leave events, in accordance with one embodiment of the presentinvention.

DETAILED DESCRIPTION

Reference will now be made in more detail to the preferred embodimentsof the present invention, systems and methods for adaptive zoneallocation based on traffic demand from one or more remote stations in awireless backhaul network. While the invention will be described inconjunction with the preferred embodiments, it will be understood thatthey are not intended to limit the invention to these embodiments. Onthe contrary, the invention is intended to cover alternatives,modifications and equivalents which may be included within the spiritand scope of the invention.

Accordingly, embodiments of the present invention provide for adaptiveresource allocation within a wireless backhaul network. Otherembodiments provide the above advantage and also provides for adaptivezone allocation for downlink traffic in a wireless backhaul network.

While embodiments of the present invention are described as providingadaptive zone allocation within a wireless backhaul network, otherembodiments are well suited to providing adaptive resource allocationfor any type of communication network, wireless or wired, and/or betweennodes of any communication network.

Notation and Nomenclature

Embodiments of the present invention can be implemented on a softwareprogram for processing data through a computer system. The computersystem can be a personal computer, notebook computer, server computer,mainframe, networked computer (e.g., router), handheld computer,personal digital assistant, workstation, and the like. Other embodimentsmay be implemented through specialized hardware for purposes ofimplementing adaptive zone allocation within a communication network,such as a wireless backhaul network. This program or its correspondinghardware implementation is operable for enabling the integration of oneor more applications supporting the completion or implementation of awork flow process. In one embodiment, the computer system includes aprocessor coupled to a bus and memory storage coupled to the bus. Thememory storage can be volatile or non-volatile and can include removablestorage media. The computer can also include a display, provision fordata input and output, etc.

Some portions of the detailed descriptions that follow are presented interms of procedures, steps, logic block, processing, and other symbolicrepresentations of operations on data bits that can be performed oncomputer memory. These descriptions and representations are the meansused by those skilled in the data processing arts to most effectivelyconvey the substance of their work to others skilled in the art. Aprocedure, computer executed step, logic block, process, etc. is here,and generally, conceived to be a self-consistent sequence of operationsor instructions leading to a desired result. The operations are thoserequiring physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a computer system. It has provenconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the present invention,discussions utilizing terms such as “determining,” “creating,”“storing,” or the like refer to the actions and processes of a computersystem, or similar electronic computing device, including an embeddedsystem, that manipulates and transfers data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Further, throughout the Application, the term “database” is used todescribe a location for storing information or data, and/or a mechanismfor storing information or data. As such, “database” is interchangeablewith the following terms: storage, data store, etc.

Exemplary Implementation of Backhaul Scheduling

Some communication systems use a time division duplex (TDD) framestructure. The frame can be divided into downlink (DL) and uplink (UL)sub-frames. The DL sub-frame starts with one preamble symbol, followedby two symbols used for DL/UL MAP. The UL sub-frame also starts withthree symbols used for UL ranging. The remaining parts of thesesub-frames are used to carry DL/UL traffic to/from connected remotestations. They are divided into a number of time sectors or “zones”,where each zone is allocated to one remote station. Each zone spans anumber of symbols in time domain and all sub-channels in frequencydomain. A symbol can be defined as the smallest increment of timeallocation in the TDD frame structure or protocol.

In one implementation, both the DL and UL sub-frames use partial usagesubchannelization (PUSC) permutation. As a result, the minimum unit ofallocation in time domain for both sub-frames (referred to as “slot”) istwo symbols. While embodiments of the present invention are describedusing a minimum unit of allocation of two symbols, other embodiments ofthe present invention are well suited to requiring no minimum units ofallocation in the time domain for sub-frames.

Backhaul scheduling involves a combination of inter-zone and intra-zonescheduling. For inter-zone scheduling, each connected remote station isassigned a zone in DL and UL sub-frames. The size of the zones in timedomain depends on the system configuration (e.g., frame duration andDL/UL TDD split) and the number of connected remotes.

For intra-zone scheduling, within each zone, the scheduler goes throughthe list of service flows associated with its corresponding remotestation and tries to schedule their packets according to their Qualityof Service (QoS) requirements, in one implementation.

As part of the intra-zone scheduling, the scheduler divides each zoneinto a number of bursts (currently only two bursts are configured; onefor management and one for data). Each burst spans one or moresub-channels in frequency domain and all symbols of the zone in timedomain. The management bursts can be used for backhaul control channelsand carry management messages in DL and feedback control messages in UL.

As previously described, static allocation of DL/UL zones through abackhaul network is based on the number of connected remote stations.Upon each network entry or leave of a remote station, available DL/ULradio resources are equally partitioned among the connected remotestations. That is, the hub station tries to give remotes equal portionsof DL/UL symbols, to the extent that is possible given even number ofsymbols in each PUSC zone. As such, the zone allocation remains staticas long as the number of connected remote stations does not change.

For purposes of illustration, zone allocation may be performed by amapping function. The mapping function is called each time a frame zonestructure needs to be altered. In one implementation these occasionshappen when a remote station joins or leaves the hub station. Themapping function can use a DL or UL table to determine the number ofDL/UL symbols allocated for each remote station. These tables can belook-up tables that contain two dimensional arrays representing thenumber of connected remote stations and the granting of symbols for eachremote station.

Adaptive Zone Allocation in a Backhaul Network

As previously mentioned static zone allocations are changed only onnetwork entry and leave of a remote station, and also gives remotestations equal portions of radio resources independent of their trafficactivity. On the other hand, embodiments of the present inventionprovide for a dynamic resource allocation process that takes the trafficdemands at each of the remote stations into consideration and adaptsallocated radio resources to the traffic activity of remote stations.

Accordingly, embodiments of the present invention provide for anadaptive zone allocation algorithm that monitors remote stations trafficactivity on a periodic basis and dynamically adjusts zone sizes based onthe traffic demand at the remote stations. In particular, inter-zonescheduling is performed based on a new adaptive solution that changeszone allocations much more frequently than the previously describedstatic implementation of zone allocation.

While embodiments of the present invention provide for zone allocationin a frame structure that is divided between inter-zone and intra-zonescheduling, other embodiments are well suited to a dynamic schedulingsolution that uses the whole DL/UL sub-frames to serve data traffic fromremote stations, with no zone partitioning and no minimum allocation.Application of the principles outlined in this application are wellsuited to the above dynamic scheduling solution.

For purposes of illustration only, embodiments of the system and methodfor adaptive zone allocation in a wireless backhaul network describedherein include the size of each DL/UL zone as being an even number ofsymbols, with a minimum of two. Of course, in other embodiments, thesize of the DL/UL zone can be an even or odd number of symbols and mayalso be zero.

FIG. 1 shows the DL zone allocation numerology with 16 data symbols inDL sub-frame. In one implementation, it is assumed in this table thatremote stations are sorted based on their DL traffic activity indescending order and numbered accordingly. So, in each traffic activityscenario (each row of the table), remote station 1 would be the mostactive remote in DL direction, remote station 2 is the second mostactive one, and so on. Following the above example, the number ofallocation choices as shown in FIG. 1 (e.g., with 16 DL symbols) islimited to five choices for any given number of connected remotestations.

Embodiments of the present invention are described within the context ofproviding adaptive zone allocation in the DL direction (e.g., allocationof DL sub-frames); however, other embodiments are well suited toproviding adaptive zone allocation in the UL direction (e.g., allocationof UL sub-frames), depending on requirements.

In one embodiment, traffic demand of remote stations is monitored. Forinstance, the average DL traffic demand of connected remote stations istracked or monitored. This can be done, for instance, by monitoring theDL queue size of best effort (BE) traffic and keeping track of committedrate (CIR) of higher priority (HP) traffic. The averaging shall be doneon a periodic basis with a configurable period.

For instance, traffic is monitored using DL traffic queue size as theindicator of the DL traffic demand. If the size of the DL traffic queuefor a particular remote station starts growing over time, it indicatesthat the DL traffic demand of that remote unit is higher than itscurrent DL bandwidth allocation.

In one implementation, for each remote station, the scheduler maintainsseparate queues for DL flows in different QoS classes. To get anestimate of overall DL traffic demand for a remote node, the featurewill determine a queue length metric using DL queue sizes over variousQoS classes. In one application, HP flows are given a higher weight overlesser priority flows, such as BE traffic. As such, in oneimplementation the queue length metric for each remote unit iscalculated as, in Equation 1, as follows:L _(i)=(w _(HP))(L _(HP-aggregate))+L _(BE-aggregate)  (1)

where L_(HP-aggregate) and L_(BE-aggregate) are the aggregate queuesizes over all DL HP and BE queues for remote node i, respectively, andw_(HP) is an integer representing the weight given to the higherpriority (HP) traffic relative to BE traffic.

In addition, DL queue sizes are sampled periodically with a period ofT_(q) frames. The average queue length metric is then calculated usingthe sampled queue sizes, as follows in Equation 2:L _(i)(k)=∝·L _(i)(k)+(1−∝)· L _(i)(k−1)  (2)where:

-   -   L_(i)(k) is the queue length metric for remote unit i at k-th        queue size sampling instance,    -   L _(i)(k) is the average queue length metric for remote unit i        at k-th queue size sampling instance, and    -   ∝ is a configurable parameter that ranges between zero and one.

The sampling time of the DL queue length metric (T_(q) frames) will bemuch shorter than the period of the adaptive allocation algorithm, inone implementation. This allows a number of queue size samples to becollected during each period of adaptive allocation. The average queuelength metric in this case represents the short-term average trafficdemand of a remote node.

After measuring the average DL queue length metric for each remote node,these values can be mapped to three levels of DL traffic demand: low,medium and high. The mapping is done using two configurable thresholdson the average queue length metric: L_(low) and L_(high), withL_(low)≦L_(high). For each remote unit i, the DL traffic demand isdetermined, as follows:Low traffic demand: if L _(i) <L _(low).Medium traffic demand: if. L _(low) ≦ L _(i) ≦L _(high); andHigh traffic demand: if L _(i) >L _(high).

In another embodiment, DL symbols are reallocated among connected remotestations according to their average DL traffic demand of remote nodesfor purposes of adaptive zone allocation. The zone reallocation is doneon a periodic basis with a configurable period of T_(a). In oneembodiment, the following process is performed for adaptive zoneallocation.

-   -   1. If there is only one remote node in the system, it gets all        DL symbols and the process ends.    -   2. Assigning minimum allocation to remote nodes. The initial        size of each DL zone is set to 2 symbols.    -   3. If there is more than one remaining DL symbols after minimum        allocation, the process continues with adaptive allocation.        Otherwise, it ends at this step.    -   4. Adaptive allocation of remaining symbols based on traffic        demand. Depending on the current levels of DL traffic demand,        one of the following can happen:        -   a. If all connected remote units have the same level of DL            traffic demand, the remaining symbols are assigned using a            simple round-robin method. In each round, each remote node            is assigned 2 additional DL symbols. This process continues            until all available DL symbols are assigned.        -   b. If remote nodes have different levels of DL traffic            demand, a two-level round-robin method is used to assign            remaining symbols.            -   i. First, each remote node with high traffic demand gets                2 additional DL symbols.            -   ii. Then, each remote unit with high or medium traffic                demand gets 2 more DL symbols.            -   iii. The above steps are repeated until all DL symbols                are assigned.

For example, the two-level round-robin method described in step 4(b)above is equivalent to a weighted round-robin resource allocation methodwith weights of 2 and 1 for high and medium traffic demands.

In addition, each of the round-robin loops mentioned above can have itsown pointer that points to the remote unit that will be served next inthe loop. For instance, one pointer can be used in the round-robin loopof step 4(a), and two pointers can be used in the two round-robin loopsof step 4(b). In one implementation the current values of these pointersare maintained at the end of each allocation process when all symbolsare allocated. Thus, in the next instance of adaptive zone allocation,each round-robin loop starts its allocation at the remote unit pointedby its own pointer (that is carried forward from the previous instanceof running the loop).

For the two-level round-robin method mentioned in step 4(b), eachallocation instance starts with the first loop (the one in step (i)).Therefore, even if an allocation process ends in the middle of thesecond loop, the next instance of symbol allocation starts with thefirst loop at the remote unit indicated by the first loop pointer. Thiscan ensure higher allocation for the remote stations with high trafficdemand.

FIG. 2 shows an exemplary pseudocode for the adaptive zone allocationalgorithm, in one embodiment. As input, it takes the number of DLsymbols, the number of connected remote units, and the list of trafficdemands of remotes. In one implementation traffic demands of low, mediumand high are mapped to 0, 1, and 2, respectively. The current settingsof input parameters in this code are just shown as an example. Oneassumption is that these parameters are updated based on the currentstate of the system.

The mod( ) function in FIG. 2 is the modulus function. In this case, itcan be implemented as:

$\begin{matrix}{{{mod}\left( {p,N} \right)} = {{p\mspace{14mu}{if}\mspace{14mu} p} < N}} \\{= {0\mspace{14mu}{otherwise}}}\end{matrix}$

The “ones(1,N)” function in this code creates a vector of length N withall elements set to one. It is used for the initial setting of the DLallocation vector, in one implementation. The “sum( )” function returnsthe sum of elements of the vector, while “min( )” and “max( )” returnthe minimum and maximum values of elements of the vector.

The output of this algorithm is the list of DL zone allocations storedin “dlzone.” As an example, for the input setting mentioned in this code(e.g., 16 symbols and 4 remotes with traffic demand of [1, 2, 1, 0]),the zone allocation vector would be equal to [4, 6, 4, 2] symbols inevery DL allocation instance as long as the traffic demand scenarioremains unchanged. If the traffic demand scenario changes to [2, 2, 1,0] (i.e., if first remote node is marked as high traffic demand), theallocation vector is updated to the following sequences of values duringthe next three DL allocation instances:

[6, 6, 2, 2];[6, 4, 4, 2]; and[4, 6, 4, 2].

This pattern would then be repeated while the traffic demand conditionremains the same.

In the event the fourth remote node leaves the system and the trafficscenario for the remaining remote nodes remains unchanged, the new inputvector would change to [2, 2, 1]. The DL allocation in this case changesto [6, 6, 4] symbols for the remaining three remote nodes and remainsthe same as long as the traffic demand has not changed.

FIG. 3 shows the flowchart of actions that would take place on every newframe with regard to zone allocation, in accordance with one embodimentof the present invention. These actions include periodic monitoring oftraffic demands and periodic update of DL zone allocations. The blocklabeled as “Update adaptive DL zone allocations” performs the adaptiveallocation algorithm described in FIG. 2.

In still another embodiment, adaptive zone reallocation is performed onnetwork entry and leave. For example, a DL zone is allocated to a remotestation on its network entry and readjusts the DL allocation of existingremotes, in one embodiment. When a remote station leaves the system, itsDL resources are released and allocated to existing remote stations.

In particular, a DL zone is allocated to a remote station on its networkentry, and readjusts the DL allocation of existing remotes. In theabsence of a DL traffic demand estimate for the new remote station, thefeature assumes medium DL traffic demand at entry time for the newremote node, in one implementation. It then performs the zone allocationprocess described above at the remote node entry time to determine itsinitial zone allocation. The remote unit is then added to the list ofconnected remote stations and the feature starts monitoring its DLtraffic demand. In one implementation, the initial value for the newremote node is set to zero.

When a remote station leaves the system, the DL symbols previouslyallocated to that remote station are released. Those released DL symbolsare then allocated to existing remote stations. This is done by removingthe remote node from the list of connected remote stations and rerunningthe DL zone allocation process as described above.

The DL zone reallocation at remote node entry and leave described abovedoes not impact the periodic DL zone allocation process, in oneembodiment. The adaptive algorithm continues its periodic operation andits timing is not impacted as remote nodes enter or leave the system.This means that the DL allocation process happens both on a periodicbasis and on remote nodes entry and leave, and the same algorithm isperformed in both events.

FIG. 4 shows the flowchart of actions that would take place in theremote node entry and leave events, in accordance with one embodiment ofthe present invention. If adaptive allocation is enabled and any remoteunit(s) is left in the system after these events, the adaptive DL zoneallocation algorithm described in FIG. 2 is performed in the last step.

As described above, the adaptive allocation capabilities can be appliedduring inter-zone scheduling to adapt the size of DL zones to trafficdemand. In other embodiments the adaptive allocation capabilities canalso be turned on or off, depending on local requirements. For instance,it may be necessary to disable adaptive allocation capabilities duringtesting.

When adaptive allocation is turned off, static allocation of resourcesmay be implemented, as previously described, in one embodiment. That is,allocation is performed statically with almost equal portions per remotenodes. Also, DL traffic demands are not monitored. In addition, theperiodic DL zone allocation process is disabled. As a result, the DLallocation will be done only on network entry and leave using a DLlook-up table, for example.

On the other hand, when adaptive allocation is turned on, DL symbols arereallocated using the adaptive allocation solution of variousembodiments of the present invention. For instance, the initialallocation in this case may be based on medium traffic demand assumptionfor all remote stations. At the same time, the feature starts monitoringDL traffic demand of remote nodes and initiates the periodic DLreallocation process. In other implementations, the initial value forthe all connected remote units may be set to zero in accordance with alow traffic demand assumption for all remote stations.

In still another embodiment, air interface quality-of-service (QoS)supports two QoS classes for each remote station. For instance, oneclass could be best effort, and another is a higher priority class(e.g., a generalized second class). For traffic given higher priority,the main QoS requirement is the committed information rate (CIR). The DLresource allocation should provide higher priority for higher priorityflows over BE traffic, in one embodiment. Otherwise, remote units withhigh amount of higher priority traffic will be treated in the same wayas remote nodes with high BE traffic demand, and could end up withsimilar or lower DL allocation than remote stations with BE traffic.

The higher priority traffic is supported both at inter-zone allocationand intra-zone scheduling levels, in one embodiment. As an example, foreach remote station, separate queues are used for different QoS classes.Within each zone, the scheduler first allocates bandwidth to the higherpriority flows according to their CIR requirements. Then, the remainingbandwidth is used to serve packets from BE flows as well as higherpriority class flows having traffic demand above their CIRs. As such,the adaptive zone allocation supports higher priorities for differentclasses of traffic.

Table 1 lists configuration parameters used by one implementation of theadaptive resource allocation solution, such as within a wirelessbackhaul network, in accordance with one embodiment of the presentinvention. In one implementation, these parameters are configuredthrough a command line interface (CLI) of a corresponding system. TheseCLIs allow the setting of parameters in run time, as well as performinga reading of values.

TABLE 1 Configuration parameters used by adaptive resource allocationfeature Parameter Description Type Configurability Enable For turningon/off the adaptive Boolean Run time adaptive DL zone allocationallocation T_(q) Time period for sampling Integer Run time queue sizesand updating average queue length metrics (in frames) W_(HP) Weight ofHP queue size, Integer Run time relative to that of BE ∝ Constant usedin calculating Floating Run time the average queue length metric, pointranging between 0 and 1 L_(low) Low threshold on average queue IntegerRun time length metric (in bytes) L_(high) High threshold on averagequeue Integer Run time length metric (in bytes) T_(a) Time period foradaptive DL zone Integer Run time reallocation (in frames)

A system and method for adaptive resource allocation within acommunication network, such as adaptive zone allocation in a wirelessbackhaul network, is thus described. While the invention has beenillustrated and described by means of specific embodiments, it is to beunderstood that numerous changes and modifications may be made thereinwithout departing from the spirit and scope of the invention as definedin the appended claims and equivalents thereof. Furthermore, while thepresent invention has been described in particular embodiments, itshould be appreciated that the present invention should not be construedas limited by such embodiments, but rather construed according to thebelow claims.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

The one or more present inventions, in various embodiments, includecomponents, methods, processes, systems and/or apparatus substantiallyas depicted and described herein, including various embodiments,subcombinations, and subsets thereof. Those of skill in the art willunderstand how to make and use the present invention after understandingthe present disclosure.

The present invention, in various embodiments, includes providingdevices and processes in the absence of items not depicted and/ordescribed herein or in various embodiments hereof, including in theabsence of such items as may have been used in previous devices orprocesses (e.g., for improving performance, achieving ease and/orreducing cost of implementation).

The foregoing discussion of the invention has been presented forpurposes of illustration and description. The foregoing is not intendedto limit the invention to the form or forms disclosed herein. In theforegoing Detailed Description for example, various features of theinvention are grouped together in one or more embodiments for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the claimed inventionrequires more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive aspects lie in less than allfeatures of a single foregoing disclosed embodiment. Thus, the followingclaims are hereby incorporated into this Detailed Description, with eachclaim standing on its own as a separate preferred embodiment of theinvention.

Moreover, though the description of the invention has includeddescription of one or more embodiments and certain variations andmodifications, other variations and modifications are within the scopeof the invention (e.g., as may be within the skill and knowledge ofthose in the art, after understanding the present disclosure). It isintended to obtain rights which include alternative embodiments to theextent permitted, including alternate, interchangeable and/or equivalentstructures, functions, ranges or steps to those claimed, whether or notsuch alternate, interchangeable and/or equivalent structures, functions,ranges or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

What is claimed is:
 1. A system comprising: a central communicationstation configured to communicate with a plurality of remotecommunication stations and to communicate with one or more systemsoutside of a network including said central communication station andsaid plurality of remote communication stations, wherein said centralcommunication station is configured to use an adaptive resourceallocation protocol to allocate zones of time frames to remotecommunication stations of said plurality of remote communicationstations in accordance with both communication traffic demand of saidremote communication stations and a preselected standard of service,wherein said time frames are used for communications between saidcentral communication station and said plurality of remote communicationstations, and wherein a first remote communication station of saidplurality of remote communication stations communicates with saidcentral communication station during zones of said time frames allocatedto the first remote communication station using said adaptive resourceallocation protocol.
 2. The system of claim 1, wherein said adaptiveresource allocation protocol provides for allocating a zone of said timeframes having at least a minimum size to each remote communicationstation of said plurality of remote communication stations andallocating remaining zones of said time frames to provide an increasedsize zone to particular remote communication stations of said pluralityof remote communication stations in accordance with current levels ofcommunication traffic demand of the respective remote communicationstations.
 3. The system of claim 2, wherein said current levels ofcommunication traffic demand comprise current levels of download trafficdemand.
 4. The system of claim 2, wherein said minimum size of saidzones comprises one or more symbols.
 5. The system of claim 4, whereinsaid minimum size of said zones comprises two symbols.
 6. The system ofclaim 1, wherein said frames have an upload subframe and a downloadsubframe, and wherein said zones allocated to said first remotecommunication station in accordance with said adaptive resourceallocation protocol include a zone in said upload subframe and a zone insaid download subframe.
 7. The system of claim 1, wherein said centralcommunication station comprises a hub of a wireless backhaul networkproviding backhaul communication between said plurality of remotecommunication stations and said one or more systems outside of thenetwork including said central communication station and said pluralityof remote communication stations.
 8. The system of claim 1, wherein saidpreselected standard of service comprises a standard volume of stationcommunications between said central communications station and a remotecommunications station, and wherein said allocation of zones inaccordance with said communication traffic demand provides an increasein a size of a particular zone allocated to a particular remotecommunication station in proportion to a difference of demand above saidstandard of service.
 9. The system of claim 1, wherein said allocationof zones in accordance with communication traffic demand provides anincrease in a size of a particular zone allocated to a particular remotecommunication station based at least in part on a priority of saidcommunication traffic demand associated with said particular remotecommunication station.
 10. The system of claim 9, wherein said prioritycomprises a quality of service class.
 11. The system of claim 9, whereinsaid allocation of zones in accordance with communication traffic demandprovides an increase in a size of said particular zone allocated to saidparticular remote communication station based at least in part on a sizeof said communication traffic demand associated with said particularremote communication station.
 12. The system of claim 11, wherein saidpriority of said communication traffic demand is weighted more heavilythan said size of said communication traffic demand in providing saidincrease in said size of said particular zone allocated to saidparticular remote communication station.
 13. A system comprising: afirst remote communication station of a plurality of remotecommunication stations configured to communicate with a centralcommunication station for communicating with one or more systems outsideof a network including said central communication station and saidplurality of remote communication stations, wherein said first remotecommunication station is configured to communicate with said centralcommunication station using an adaptive resource allocation protocolconfigured to allocate zones of time frames to remote communicationstations of said plurality of remote communication stations inaccordance with both communication traffic demand of said remotecommunication stations and a preselected standard of service, andwherein said first remote communication station communicates with saidcentral communication station during zones of said time frames allocatedto the first remote communication station using said adaptive resourceallocation protocol.
 14. The system of claim 13, wherein said adaptiveresource allocation protocol provides for allocating a zone of said timeframes having at least a minimum size to each remote communicationstation of said plurality of remote communication stations andallocating remaining zones of said time frames to provide an increasedsize zone to remote communication stations of said plurality of remotecommunication stations in accordance with current levels ofcommunication traffic demand of the respective remote communicationstations.
 15. The system of claim 14, wherein said current levels ofcommunication traffic demand comprise current levels of download trafficdemand.
 16. The system of claim 14, wherein said minimum size of saidzones comprises one or more symbols.
 17. The system of claim 16, whereinsaid minimum size of said zones comprises two symbols.
 18. The system ofclaim 13, wherein said time frames have an upload subframe and adownload subframe, and wherein said zones allocated to said first remotecommunication station using said adaptive resource allocation protocolinclude a zone in said upload subframe and a zone in said downloadsubframe.
 19. The system of claim 13, wherein said network includingsaid central communication station and said plurality of remotecommunication stations comprises a wireless backhaul network.
 20. Thesystem of claim 13, wherein said preselected standard of servicecomprises a standard volume of station communications between saidcentral communications station and a remote communications station, andwherein said allocation of zones in accordance with communicationtraffic demand provides an increase in a size of said zones allocated tosaid first remote communication station in proportion to a difference ofdemand above said standard of service.
 21. The system of claim 13,wherein said allocation of said zones in accordance with communicationtraffic demand provides an increase in a size of said zones allocated tosaid first remote communication station based at least in part on apriority of said communication traffic demand associated with said firstremote communication station.
 22. The system of claim 21, wherein saidpriority comprises a quality of service class.
 23. The system of claim21, wherein said allocation of zones in accordance with communicationtraffic demand provides an increase in a size of said zones allocated tosaid first remote communication station based at least in part on a sizeof said communication traffic demand associated with said first remotecommunication station.
 24. The system of claim 23, wherein said priorityof said communication traffic demand is weighted more heavily than saidsize of said communication traffic demand in providing said increase insaid size of said zones allocated to said first remote communicationstation.
 25. An adaptive resource allocation method comprising:allocating, in communications between a central communication stationand a plurality of remote communication stations using time frames, azone of said time frames having at least a minimum size to each remotecommunication station of said plurality of remote communicationstations; and allocating, in said communications between said centralcommunication station and said plurality of remote communicationstations using said time frames, remaining zones of said time frames toprovide an increased size zone to one or more remote communicationstations of said plurality of remote communication stations inaccordance with current levels of communication traffic demand of therespective remote communication stations.
 26. The method of claim 25,wherein said allocating said zone of said time frames having at least aminimum size to each remote communication station allocates zones oftime frames to remote communication stations in accordance with apreselected standard of service, and wherein said allocating saidremaining zones of said time frames to provide said increased size zoneto said remote communication stations allocates zones of time frames toremote communication stations in accordance with communication trafficdemand of said remote communication stations.
 27. The method of claim25, wherein said allocating said remaining zones of said time frames toprovide said increased size zone to said remote communication stationsprovides an increase in a size of said zone allocated to a particularremote communication station based at least in part on a priority ofsaid communication traffic demand associated with said particular remotecommunication station.
 28. The method of claim 27, wherein said prioritycomprises a quality of service class.
 29. The method of claim 27,wherein said allocating said remaining zones of said time frames toprovide said increased size zone to said remote communication stationsprovides an increase in a size of said zone allocated to said particularremote communication station based at least in part on a size of saidcommunication traffic demand associated with said particular remotecommunication station.
 30. The method of claim 29, wherein said priorityof said communication traffic demand is weighted more heavily than saidsize of said communication traffic demand in providing said increase insaid size of said zone allocated to said particular remote communicationstation.
 31. The method of claim 25, further comprising: monitoringcommunication traffic activity of said plurality of remote communicationstations on a periodic basis, wherein said allocating remaining zones ofsaid time frames is based upon said monitored communication trafficactivity.
 32. The method of claim 31, wherein said monitoringcommunication traffic activity comprises: monitoring download trafficqueue size for said plurality of remote communication stations.
 33. Themethod of claim 32, wherein said monitoring download traffic queue sizecomprises: determining a queue length metric using download queue sizesover various quality of service classes.
 34. The method of claim 31,further comprising: mapping said monitored traffic activity of saidplurality of remote communication stations to 3 levels of downloadtraffic demand, said 3 levels of download traffic demand consisting oflow, medium, and high.
 35. The method of claim 25, wherein saidallocating remaining zones of said time frames comprises: if all saidremote communication stations of said plurality of remote communicationstations having communication traffic demand have a same level ofdownload traffic demand, implementing a round-robin assignment protocolto allocate said remaining zones of said time frames to each remotecommunication station of said plurality of remote communication stationshaving communication traffic demand; and if remote communicationstations of said plurality of remote communication stations havingcommunication traffic demand have a different level of download trafficdemand, implementing a two-level round-robin assignment protocol toallocate said remaining zones of said time frames first to each remotecommunication station with high traffic demand and then to each remoteunit with high or medium traffic demand.